---
title: "Untitled"
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    vertical_layout: fill
    logo: logo.png
    source_code: embed
    social: menu
---

```{r setup, include=FALSE}
library(flexdashboard)
```

```{r global, include=FALSE}
# load data in 'global' chunk so it can be shared by all users of the dashboard
library(flexdashboard)
library(tidyverse)
library(plotly)
library(shiny)
library(packrat)
library(rsconnect)
library(knitr)
library(DT)
library(ggplot2)
library(dplyr)
df <- read_csv("https://aileen-charron.github.io/M460_Statistical_Analysis_with_R/food_order.csv")

df <- df %>% 
  filter(rating != "Not given")


df_summary <- df %>%
  group_by(restaurant_name, cuisine_type) %>%
  summarise(
    avg_cost_of_the_order = round(mean(cost_of_the_order, na.rm = TRUE), 2),  
    avg_rating = round(mean(as.numeric(rating), na.rm = TRUE), 2),
    avg_prep_time = round(mean(food_preparation_time, na.rm = TRUE), 0),  
    avg_delivery_time = round(mean(delivery_time, na.rm = TRUE), 0),  
    avg_total_time = round(avg_prep_time + avg_delivery_time, 0),     
    num_ratings = sum(!is.na(rating) & rating != "Not given") 
  ) %>%
  select(
    restaurant_name, cuisine_type, avg_cost_of_the_order, avg_rating, 
    avg_prep_time, avg_delivery_time, avg_total_time, num_ratings  
  )


top_5 <- df_summary %>% 
  arrange(desc(num_ratings)) %>% 
  head(5)

p1 <-top_5 %>%  ggplot(aes(x = reorder(restaurant_name, -num_ratings), y = num_ratings))+
    geom_bar(stat = "identity") +
    labs(
      x = "Restaurant Name",
      y = "Number of Ratings"
    )+
    theme(axis.text.x = element_text(angle = 45, hjust = 1))


p2 <- top_5 %>% ggplot(aes(x = reorder(restaurant_name, -num_ratings), y = avg_rating)) + 
           geom_bar(stat = "identity") +
           labs(x = "Restaurant Name",
                y = "Average Rating") +
           theme(axis.text.x = element_text(angle = 45, hjust = 1))

p3 <- top_5 %>% ggplot(aes(x = reorder(restaurant_name, -num_ratings), y = avg_cost_of_the_order)) + 
           geom_bar(stat = "identity") +
           labs(x = "Restaurant Name",
                y = "Average Cost of Order") +
           theme(axis.text.x = element_text(angle = 45, hjust = 1))

p4 <- top_5 %>% ggplot(aes(x = reorder(restaurant_name, -num_ratings), y = avg_total_time)) + 
           geom_bar(stat = "identity") +
           labs(x = "Restaurant Name",
                y = "Average Total Time") +
           theme(axis.text.x = element_text(angle = 45, hjust = 1))
```

#  {.sidebar}

NYC Restaurants Data - Food Ordering and Delivery. Looking to see how ratings (1-5) vary by other factors.

# Restaurant List

## Column {data-width="1000"}

### searchable table

```{r}
datatable(df, options = list(
  pageLength = 100
))
```

# Top 5 Restaurants

## Column {data-width="500"}

### Top 5 Restaurants by Number of Ratings

```{r}
p1
```

## Column {data-width="500"}

### Average Rating of Top 5 Restaurants

```{r}
p2
```

# Costs & Times

## Column {data-width="500"}

### Average Cost of Top 5 Restaurants

```{r}
p3

```

## Column {data-width="500"}

### Average Cost of Top 5 Restaurants

```{r}
p4

```